I’ve built multi-asset staking systems before and the trick is to make rewards accounting flexible while keeping the asset pools free to move (no hard locks). Below I give a set of practical strategies I use (why, pros/cons), concrete Move patterns you can drop into Sui, and an operational/testing checklist. I write in first person and include multiple code snippets so you can pick the pattern(s) that fit your product.

High-level strategies

1. Per-asset share tokens + accRewardPerShare (lazy accounting). Very gas-efficient; pools remain unlocked.
2. Boost multipliers (ve-token / tenure) applied at claim time. Dynamic weights without locking underlying LPs.
3. Virtual balances / strategy adapters so underlying assets can be rebalanced while user shares remain stable.
4. Off-chain accrual + Merkle claims for ultra-high-frequency micro-rewards. Publish roots on-chain, users prove entitlement.
5. Reward policy objects (on-chain pluggable rules) to change distribution formulas without migrating state.
6. Emergency redeem / queued withdrawals to let users extract when off-chain strategies are illiquid.

I typically combine (1) + (2) + (3) for a production staking vault: share tokens + lazy accruals + boosts + strategy adapters.

1) Per-asset shares + accRewardPerShare (the core pattern)

Why I use it: avoid iterating over all LP positions on every reward update. I update an accumulator when rewards arrive; users claim lazily (compute owed = shares * delta(acc) - reward_debt).

Pros: very gas efficient for writes; claims are O(1). Cons: claim tx touches user share object + pool object; must avoid rounding errors.

Move sketch (core accounting)

module staking::simple {
    use sui::object::{UID};
    use sui::tx_context::TxContext;
    use std::vector;

    const PRECISION: u128 = 1_000_000_000u128;

    struct Pool has key {
        id: UID,
        asset_type: vector<u8>, // token identifier
        total_shares: u128,
        acc_reward_per_share: u128, // scaled by PRECISION
    }

    // Each user's stake represented by an owned share object
    struct Share has key {
        id: UID,
        owner: address,
        shares: u128,
        reward_debt: u128, // shares * acc_reward_per_share at deposit time
    }

    // Add reward to pool (called by strategy or distributor)
    public(entry) fun deposit_rewards(pool: &mut Pool, reward: u128) {
        assert!(pool.total_shares > 0, 1);
        pool.acc_reward_per_share = pool.acc_reward_per_share + (reward * PRECISION / pool.total_shares);
    }

    // User deposits underlying and receives share object (minted)
    public(entry) fun deposit(pool: &mut Pool, owner: address, amt_shares: u128, ctx: &mut TxContext): Share {
        pool.total_shares = pool.total_shares + amt_shares;
        let id = object::new(ctx);
        let debt = pool.acc_reward_per_share * amt_shares / PRECISION;
        Share { id, owner, shares: amt_shares, reward_debt: debt }
    }

    // Claim pending rewards
    public(entry) fun claim(share: &mut Share, pool: &Pool) {
        let pending = (pool.acc_reward_per_share * share.shares / PRECISION) - share.reward_debt;
        // transfer pending to share.owner (implementation-specific)
        // update reward_debt
        share.reward_debt = pool.acc_reward_per_share * share.shares / PRECISION;
    }

    // Withdraw: claim + burn shares, decrease total_shares
    public(entry) fun withdraw(share: Share, pool: &mut Pool) {
        // claim logic then burn share (object deleted) and reduce pool.total_shares
    }
}

Important details I always enforce

• Use fixed PRECISION to avoid fractional truncation issues.
• Store reward_debt per-share at deposit/withdraw to compute owed correctly.
• When you change acc_reward_per_share ensure total_shares > 0, otherwise stash rewards to a buffer.

2) Dynamic boosts (ve-token, tenure, NFT boosts) — apply at claim time

I do not permanently alter shares. Instead I compute an effective share at claim time:

effective_shares = base_shares * (1 + boost_multiplier)

This keeps the pool unlocked (shares remain stable) and allows dynamic reward distribution dependent on external state (locked ve tokens, NFT badges, staking age).

Pattern: store base shares + compute boost in claim

module staking::boosted {
    use staking::simple;
    use sui::tx_context::TxContext;

    // Example: a boost oracle module (off-chain/other module) provides multiplier
    public(fun) native fun get_boost(owner: address): u128;

    public(entry) fun claim_with_boost(share: &mut simple::Share, pool: &simple::Pool) {
        // basic pending computed on base shares
        let base_pending = (pool.acc_reward_per_share * share.shares / simple::PRECISION) - share.reward_debt;

        // fetch boost multiplier (e.g., 120% => multiplier = 120_000_000 where scale = 1_000_000)
        let boost = get_boost(share.owner);

        // effective pending = base_pending * boost / BOOST_SCALE
        let BOOST_SCALE: u128 = 1_000_000u128;
        let effective = base_pending * boost / BOOST_SCALE;

        // transfer effective to owner; update debt using base shares
        share.reward_debt = pool.acc_reward_per_share * share.shares / simple::PRECISION;
    }
}

Notes

• I make get_boost an oracle module or an on-chain VeToken module that derives boost from lock amount & lock duration.
• Because boost is applied at claim-time, you can change boost rules cheaply and retroactively (affects future claims only).

3) Virtual balances & strategy adapters (keep pools unlocked)

If your vault rebalances across strategies (Aave, yield source A, B), you should keep a virtual accounting layer. Users own shares that represent a claim on the vault, while the vault swaps and migrates real assets across strategies.

I model each strategy as an adapter object with deposit and withdraw entrypoints. When migrating, I do small, idempotent batches.

Why I do this: users can withdraw shares without needing to know where assets are invested; strategies can be rewired off-chain.

4) Off-chain accrual + Merkle claims (micro rewards)

When rewards are tiny and frequent (every block), on-chain bookkeeping is too costly. I compute per-user accruals off-chain (streaming), then publish an epoch Merkle root on-chain. Users claim by presenting a Merkle proof.

Pros: cheap on-chain for high-frequency systems. Cons: requires trust assumptions about the root publisher or a dispute/fraud-prove mechanism.

On-chain Merkle claim pattern (sketch)

module staking::merkle {
    use sui::object::{UID};
    use sui::tx_context::TxContext;

    struct EpochRoot has key { id: UID, epoch: u64, root: vector<u8>, finalized: bool }

    public(entry) fun publish_root(epoch: u64, root: vector<u8>, ctx: &mut TxContext): EpochRoot {
        EpochRoot { id: object::new(ctx), epoch, root, finalized: false }
    }

    public(entry) fun claim_with_proof(root_obj: &EpochRoot, leaf: vector<u8>, proof: vector<vector<u8>>) {
        assert!(verify_merkle_proof(leaf, proof, root_obj.root), 1);
        // decode leaf -> (user, amount) and transfer amount
    }

    native fun verify_merkle_proof(leaf: vector<u8>, proof: vector<vector<u8>>, root: vector<u8>): bool;
}

I combine this with a short dispute window and on-chain challenger bounty to reduce trust.

5) Reward policy objects (pluggable rules)

I make the distribution logic a policy object: RewardPolicy with variants (pro-rata, weighted by stake-age, exponential decay). The distributor reads the policy and updates acc_reward_per_share.

This lets governance change reward curves without migrating per-user state.

struct RewardPolicy has key { id: UID, kind: u8 /* 0=pro-rata,1=age-weighted */, params: vector<u8> }
public(entry) fun distribute(policy: &RewardPolicy, pools: vector<&mut Pool>, reward: u128) {
    match policy.kind { /* compute distribution */ }
    // update pool.acc_reward_per_share accordingly
}

I prefer this architecture because it separates accounting (stable) and policy (evolvable).

6) Emergency withdrawal behavior (non-lock UX)

If strategies become illiquid, users must still be able to withdraw. I implement:

• Fast withdraw for liquid portion (immediate),
• Queued withdrawals for remainder (filled as strategies unwind),
• Emergency redeem with a small penalty.

This preserves UX without locking pools.

7) Full example: combine accRewardPerShare + boost + lazy claim

A compact end-to-end Move sketch (synthesis):

module staking::vault {
    use staking::simple;
    use staking::boosted;
    use sui::object::{UID};
    use sui::tx_context::TxContext;

    const PRECISION: u128 = 1_000_000_000u128;
    const BOOST_SCALE: u128 = 1_000_000u128;

    // deposit_rewards, deposit, withdraw as in simple module...

    public(entry) fun claim_with_boost(share: &mut simple::Share, pool: &simple::Pool) {
        // compute base pending in integer arithmetic carefully
        let base_pending = (pool.acc_reward_per_share * share.shares / PRECISION) - share.reward_debt;
        let boost = boosted::get_boost(share.owner); // e.g., 1_200_000 => 1.2x
        let total = base_pending * boost / BOOST_SCALE;
        // transfer `total` to share.owner
        share.reward_debt = pool.acc_reward_per_share * share.shares / PRECISION;
    }
}

8) Audit & testing checklist (what I do)

• Invariant tests: total rewards distributed <= total rewards deposited. sum_user_pending + pool.buffer == total_rewards_added.
• Rounding tests: simulate edge cases where acc_reward_per_share truncation could leave dust; provide dust-faucet or accumulate to next epoch.
• Boost changes test: change boost function and ensure no retroactive double-dips.
• Migration tests: strategy adapter swaps; ensure rebalances don't lose funds.
• Fuzzing: random deposits, withdraws, reward deposits; compare on-chain accounting vs reference off-chain model.
• Concurrency tests: many parallel deposits/claims across disjoint pools to ensure no unnecessary serialization.

9) Operational notes & best practices (from my deployments)

• Keep accumulator updates atomic & minimal — one call per reward event.
• Prefer Share as an owned object per user so claims are parallelizable.
• Add small stability buffer for edge rounding (collect dust into reserve and distribute periodically).
• Document policy changes and use RewardPolicy objects so front-end can detect distribution rule changes.
• Provide clear UX: show users their pending (computed off-chain by reading pool.accReward and user reward_debt) and expected gas for claim.

TL;DR — my recommended stack

• Core: shares + accRewardPerShare for each asset pool (lazy claims).
• Add: boost multipliers applied at claim time (ve-token / tenure) to be dynamic and non-locking.
• For ultra-high-frequency micro rewards: off-chain accrual + Merkle claims.
• Always: policy objects, emergency withdraw, and rigorous tests (invariants, rounding, concurrency).

When I design multi-asset staking on Sui, I treat stakes as composable objects rather than locked balances. This lets stakers retain custody and move assets, while my staking module just tracks reward weights.

The main strategy I use is decoupling accounting from custody:

1. Position NFTs: Every stake position is an object (StakePosition) that stores (asset_type, amount, weight, entry_epoch).
2. Reward weights: Instead of fixing APR, I compute reward_weight = f(asset_type, volatility, pool_utilization) dynamically every epoch.
3. Epoch snapshots: At the end of each epoch, I snapshot total weights across assets, then proportionally distribute rewards.

The trick to not lock underlying pools is delegated accounting: the actual LP tokens or coins can remain free in DeFi usage, while users mint a staking receipt object proving their commitment. Rewards accrue on the receipt object, not by immobilizing the asset.

Move-style snippet:

struct StakePosition<AssetType> has key {
    id: UID,
    owner: address,
    amount: u64,
    entry_epoch: u64,
    weight: u64,
}

public entry fun stake<AssetType>(
    coin: Coin<AssetType>, 
    ctx: &mut TxContext
): StakePosition<AssetType> {
    let amount = coin::value(&coin);
    let weight = compute_weight::<AssetType>(amount);
    coin::burn(coin); // burn in exchange for stake receipt
    StakePosition { id: object::new(ctx), owner: tx_context::sender(ctx), amount, entry_epoch: epoch_now(), weight }
}

By burning and re-minting receipts, I decouple staking logic from liquidity use. With this pattern, rewards adjust every epoch using a dynamic weight function (e.g., higher weight for volatile or scarce assets).

This makes multi-asset staking fluid and composable, without hard locks.

As a professional Web3 developer specializing in Sui and Move, I'll outline my approach to implementing dynamic multi-asset staking pools that maintain flexibility while ensuring fair reward distribution.

Core Architecture

The foundation of my implementation relies on a modular design that separates concerns while maintaining flexibility:

struct StakingPool {
    assets: map<address, AssetInfo>,
    stakes: map<address, StakeInfo>,
    rewards: map<address, RewardInfo>,
    config: PoolConfig,
}

struct AssetInfo {
    id: ID,
    decimals: u8,
    reward_multiplier: uint128,
    min_stake: uint128,
}

struct StakeInfo {
    principal: uint128,
    reward_debt: uint128,
    last_update: u64,
    asset_type: address,
}

flowchart TD
    classDef core fill:#FF9999,stroke:#CC0000,color:#000
    classDef parallel fill:#99FF99,stroke:#00CC00,color:#000
    classDef storage fill:#9999FF,stroke:#0000CC,color:#000
    classDef oracle fill:#FFFF99,stroke:#CCCC00,color:#000

    subgraph Core["Core Components"]
        Pool["Pool Manager"]:::core
        PTB["Programmable Transaction Blocks"]:::core
    end

    subgraph Parallel["Parallel Execution Layer"]
        PE["Execution Engine"]:::parallel
        MEV["MEV Protection"]:::parallel
        DAG["DAG-based Consensus"]:::parallel
    end

    subgraph Storage["Object Storage"]
        Assets["Asset Objects"]:::storage
        LP["LP Tokens"]:::storage
        Metadata["Pool Metadata"]:::storage
    end

    subgraph Oracle["Price Feeds"]
        PO["Price Oracles"]:::oracle
        Cache["Price Cache"]:::oracle
    end

    Pool --> PTB
    PTB --> PE
    PE --> MEV
    PE --> DAG
    
    Pool --> Assets
    Pool --> LP
    Pool --> Metadata
    
    Assets <--> PO
    PO --> Cache

    %% Legend
    subgraph Legend["Legend"]
        C1["Core Components"]:::core
        P1["Parallel Processing"]:::parallel
        S1["Storage Layer"]:::storage
        O1["Oracle System"]:::oracle
    end

The diagram above illustrates the system architecture, where:

• Red components represent core system elements handling pool operations
• Green sections show the parallel processing infrastructure
• Blue represents object storage for assets and metadata
• Yellow indicates external price feed integration

Implementation Strategy

1. Dynamic Reward Distribution```move public entry fun calculate_rewards( staker: address, asset_type: address, ctx: &mut TxContext ): uint128 { let pool = borrow_global_mut(POOL_ID); let stake = pool.stakes.get(&staker).unwrap(); let asset = pool.assets.get(&asset_type).unwrap();

   // Calculate rewards based on multiple factors let base_reward = calculate_base_reward(stake.principal); let multiplier = asset.reward_multiplier; let time_factor = calculate_time_factor(stake.last_update);

   return base_reward * multiplier * time_factor; }

2. **Non-Locking Mechanism**```move
public entry fun stake(
    asset_type: address,
    amount: uint128,
    ctx: &mut TxContext
) {
    let pool = borrow_global_mut<StakingPool>(POOL_ID);
    let staker = signer_from_context(ctx);
    
    // Record stake without locking
    pool.stakes.insert(
        &staker,
        StakeInfo {
            principal: amount,
            reward_debt: 0,
            last_update: current_timestamp(),
            asset_type,
        }
    );
    
    // Emit event for external tracking
    emit_event!(StakeEvent {
        staker,
        amount,
        asset_type,
        timestamp: current_timestamp(),
    });
}

Security Considerations

1. Protection Mechanisms

• Implement flash loan prevention through temporal locks
• Use Move's formal verification for critical paths
• Maintain separate price feeds for different asset pairs

2. Gas Efficiency

• Batch similar operations within transaction blocks
• Utilize sponsored transactions for common operations
• Implement lazy state updates where possible

Performance Optimization

1. Scaling Strategy

• Split large staking operations across multiple blocks
• Implement dynamic fee adjustment based on network congestion
• Cache frequently accessed data in memory

2. Monitoring and Maintenance

• Track pool utilization metrics
• Monitor gas costs per operation
• Adjust reward parameters based on performance data

This implementation provides several key benefits:

• Flexible reward distribution without pool locking
• Efficient parallel execution through modular design
• Enhanced security through formal verification
• Scalable architecture for multiple assets

Thoroughly test the reward distribution patterns under various load conditions before deployment, as the optimal configuration may vary depending on your specific use case and expected volume.

Focusing on a more event-driven and modular architecture that emphasizes flexibility and scalability.

Core Architecture

The foundation of this implementation centers around a modular design that separates concerns while maintaining flexibility:

struct StakingPool {
    assets: map<address, AssetInfo>,
    stakes: map<address, StakeInfo>,
    rewards: map<address, RewardInfo>,
    config: PoolConfig,
}

struct AssetInfo {
    id: ID,
    decimals: u8,
    reward_multiplier: uint128,
    min_stake: uint128,
}

struct StakeInfo {
    principal: uint128,
    reward_debt: uint128,
    last_update: u64,
    asset_type: address,
}

flowchart TD
    classDef core fill:#FF9999,stroke:#CC0000,color:#000
    classDef parallel fill:#99FF99,stroke:#00CC00,color:#000
    classDef storage fill:#9999FF,stroke:#0000CC,color:#000
    classDef oracle fill:#FFFF99,stroke:#CCCC00,color:#000

    subgraph Core["Core Components"]
        Pool["Pool Manager"]:::core
        PTB["Programmable Transaction Blocks"]:::core
    end

    subgraph Parallel["Parallel Execution Layer"]
        PE["Execution Engine"]:::parallel
        MEV["MEV Protection"]:::parallel
        DAG["DAG-based Consensus"]:::parallel
    end

    subgraph Storage["Object Storage"]
        Assets["Asset Objects"]:::storage
        LP["LP Tokens"]:::storage
        Metadata["Pool Metadata"]:::storage
    end

    subgraph Oracle["Price Feeds"]
        PO["Price Oracles"]:::oracle
        Cache["Price Cache"]:::oracle
    end

    Pool --> PTB
    PTB --> PE
    PE --> MEV
    PE --> DAG
    
    Pool --> Assets
    Pool --> LP
    Pool --> Metadata
    
    Assets <--> PO
    PO --> Cache

    %% Legend
    subgraph Legend["Legend"]
        C1["Core Components"]:::core
        P1["Parallel Processing"]:::parallel
        S1["Storage Layer"]:::storage
        O1["Oracle System"]:::oracle
    end

The diagram above illustrates the system architecture, where:

• Red components represent core system elements handling pool operations
• Green sections show the parallel processing infrastructure
• Blue represents object storage for assets and metadata
• Yellow indicates external price feed integration

Implementation Strategy

1. Dynamic Reward Distribution```move public entry fun calculate_rewards( staker: address, asset_type: address, ctx: &mut TxContext ): uint128 { let pool = borrow_global_mut(POOL_ID); let stake = pool.stakes.get(&staker).unwrap(); let asset = pool.assets.get(&asset_type).unwrap();

   // Calculate rewards based on multiple factors let base_reward = calculate_base_reward(stake.principal); let multiplier = asset.reward_multiplier; let time_factor = calculate_time_factor(stake.last_update);

   return base_reward * multiplier * time_factor; }

2. **Non-Locking Mechanism**```move
public entry fun stake(
    asset_type: address,
    amount: uint128,
    ctx: &mut TxContext
) {
    let pool = borrow_global_mut<StakingPool>(POOL_ID);
    let staker = signer_from_context(ctx);
    
    // Record stake without locking
    pool.stakes.insert(
        &staker,
        StakeInfo {
            principal: amount,
            reward_debt: 0,
            last_update: current_timestamp(),
            asset_type,
        }
    );
    
    // Emit event for external tracking
    emit_event!(StakeEvent {
        staker,
        amount,
        asset_type,
        timestamp: current_timestamp(),
    });
}

Security Considerations

1. Protection Mechanisms

• Implement flash loan prevention through temporal locks
• Use Move's formal verification for critical paths
• Maintain separate price feeds for different asset pairs

2. Gas Efficiency

• Batch similar operations within transaction blocks
• Utilize sponsored transactions for common operations
• Implement lazy state updates where possible

Performance Optimization

1. Scaling Strategy

• Split large staking operations across multiple blocks
• Implement dynamic fee adjustment based on network congestion
• Cache frequently accessed data in memory

2. Monitoring and Maintenance

• Track pool utilization metrics
• Monitor gas costs per operation
• Adjust reward parameters based on performance data

This implementation provides several key benefits:

• Flexible reward distribution without pool locking
• Efficient parallel execution through modular design
• Enhanced security through formal verification
• Scalable architecture for multiple assets

Thoroughly test the reward distribution patterns under various load conditions before deployment, as the optimal configuration may vary depending on your specific use case and expected volume.

You can implement multi-asset, non-locking staking on Sui by tokenizing each staked asset into liquid staking tokens (LSTs) and running a pooled reward-engine that uses per-share (virtual balance) accounting plus a dynamic allocator that weights rewards by real-time asset value and protocol objectives; specifically, mint an LST when a user deposits (so their underlying stake keeps earning on-chain via Sui’s stake objects/SIP-6) and track each holder’s cumulative rewards using a reward-per-share index to avoid locking assets, feed in a price oracle (or TWAP) to convert different asset yields into a common unit, run an epochal rebalancer that updates allocation curves (for example increasing rewards to under-utilized assets or reducing rewards when an LST peg diverges), optionally add incentive curves or decay functions to steer capital and protect against gaming, keep withdrawal/redemption as an off-chain or on-chain burn+redeem that unwraps underlying stake without forcing long locks (use queuing or buffered redemption if chain unbonding imposes delays), enforce risk controls (slashing caps, peg monitoring, and limits per validator) and expose governance parameters so you can tune distribution rules; architect the core accounting and reward logic in Move modules so calculations are atomic and auditable, use separate accounting for protocol fees vs. gross staking rewards, and simulate reward flows before mainnet launch to tune oracle latency, rebalancing cadence and economic safety margins.